An integrated intelligent approach for optimizing process parameters in diamond turning of sc-Ge for high-quality optical surfaces

Abstract Infrared (IR) materials like single-crystal germanium (sc-Ge) are gaining attention due to their outstanding optical properties, especially in infrared (IR) applications. This study focuses on improving the surface finish of sc-Ge substrates for IR applications using a diamond turning machine (DTM). The challenges in achieving the desired surface quality are addressed through an innovative approach that combines the response surface methodology (RSM) with a teaching–learning-based optimization (TLBO) algorithm. The optimal surface quality is achieved at lower depths of cut and feed rates, along with moderate spindle speeds, resulting in smoother topography and lower surface roughness. Statistical modeling using RSM highlights TLBO’s efficacy over traditional optimization methods, achieving a 0.37 nm surface roughness (Sa) under optimised machining parameters. Experimental validation demonstrate a close agreement between the optimized and experimental values, with a deviation of 3.24%. This study provides practical insights for efficient use of DTM to enhance performance of sc-Ge optical components for IR applications, contributing to surface quality optimization in the field of ultra-precision optics fabrication.